DESCRIPTION: The long-term goal of this research is to develop a numerical algorithm for predicting the concentration and size distribution of aerosols, and to evaluate the major uncertainties in applying such a tool to occupational health problems. Solutions to current challenges such as modeling human exposure to inhalable and respirable particles, estimating the performance of size-selective aerosol samplers, and optimizing the design of ventilation systems for particulate control, are hindered by the lack of a comprehensive mathematical modeling methodology. Health effects such as chronic obstructive pulmonary disease are related to both the toxicity of the material and particle size as indicated in the size-selective sampling methodology for particles outlined in the ACGIH-Threshold Limit Value for Chemical Substances. Computational fluid dynamics (CFD) is a promising approach for these and other problems, but at present, there is not a viable method for predicting aerosol concentration fields and size distributions. There are also many sources of uncertainty in the use of CFD simulations, including the turbulence model selected, the boundary conditions imposed, and the conceptual model of reality input to the computer code. This research will: (1) provide a complimentary tool for use with CFD codes to predict aerosol concentration fields and size distributions, and (2) develop and apply a methodology to evaluate uncertainties inherent in the use of this tool for occupational and environmental exposure problems. This proposal has relevance to the NORA research priorities of: Control Technology and Personal Protective Equipment, Exposure Assessment Methods, Intervention Effectiveness, and Indoor Air. In addition it is particularly pertinent to current CDC concerns regarding the spread of infectious agents in urban (e.g., bio-terrorism), or enclosed environments (e.g., airplanes). The specific aims are to: 1) Improve our existing computer algorithm to take output from computational fluid dynamics software and make predictions of size-specific aerosol concentration fields. 2) Develop parallel implementations on large-scale machines to investigate convergence of the algorithm. 3) Assess important sources of uncertainty in the prediction of aerosol concentrations relevant to occupational health problems. These issues include, (a) time-dependent effects and turbulence in the near-wake region, (b) numerical convergence and accuracy, and (C) comparability of numerical predictions and experimental data. 4) Employ computational visualization tools to enhance interpretation of the results and to improve worker education.